Demo of Mapbox
Sean
2023-03-08
mapbox_token <- Sys.getenv("MAPBOX_PUBLIC_TOKEN")
ny_tracts <- tracts("NY",c("Kings","Bronx","Queens","Richmond","New York", cb =TRUE))## Retrieving data for the year 2021##
|
| | 0%
|
| | 1%
|
|= | 1%
|
|== | 2%
|
|== | 3%
|
|=== | 4%
|
|=== | 5%
|
|==== | 6%
|
|===== | 7%
|
|===== | 8%
|
|====== | 9%
|
|======= | 10%
|
|======= | 11%
|
|======== | 11%
|
|======== | 12%
|
|========= | 12%
|
|========= | 13%
|
|========== | 14%
|
|=========== | 15%
|
|=========== | 16%
|
|============ | 17%
|
|============ | 18%
|
|============= | 18%
|
|============= | 19%
|
|============== | 19%
|
|============== | 20%
|
|============== | 21%
|
|=============== | 21%
|
|=============== | 22%
|
|================ | 22%
|
|================ | 23%
|
|================= | 24%
|
|================= | 25%
|
|================== | 25%
|
|================== | 26%
|
|=================== | 27%
|
|=================== | 28%
|
|==================== | 28%
|
|==================== | 29%
|
|===================== | 29%
|
|===================== | 30%
|
|===================== | 31%
|
|====================== | 31%
|
|======================= | 32%
|
|======================= | 33%
|
|======================= | 34%
|
|======================== | 34%
|
|======================== | 35%
|
|========================= | 36%
|
|========================== | 37%
|
|========================== | 38%
|
|=========================== | 38%
|
|=========================== | 39%
|
|============================ | 39%
|
|============================ | 40%
|
|============================ | 41%
|
|============================= | 41%
|
|============================= | 42%
|
|============================== | 42%
|
|============================== | 43%
|
|=============================== | 44%
|
|=============================== | 45%
|
|================================ | 45%
|
|================================ | 46%
|
|================================= | 47%
|
|================================= | 48%
|
|================================== | 48%
|
|================================== | 49%
|
|=================================== | 50%
|
|=================================== | 51%
|
|==================================== | 51%
|
|==================================== | 52%
|
|===================================== | 53%
|
|====================================== | 54%
|
|====================================== | 55%
|
|======================================= | 55%
|
|======================================= | 56%
|
|======================================== | 57%
|
|========================================= | 58%
|
|========================================= | 59%
|
|========================================== | 60%
|
|=========================================== | 61%
|
|=========================================== | 62%
|
|============================================ | 62%
|
|============================================ | 63%
|
|============================================ | 64%
|
|============================================= | 64%
|
|============================================= | 65%
|
|============================================== | 65%
|
|============================================== | 66%
|
|=============================================== | 67%
|
|=============================================== | 68%
|
|================================================ | 68%
|
|================================================ | 69%
|
|================================================= | 70%
|
|================================================= | 71%
|
|================================================== | 71%
|
|================================================== | 72%
|
|=================================================== | 72%
|
|=================================================== | 73%
|
|==================================================== | 74%
|
|==================================================== | 75%
|
|===================================================== | 76%
|
|====================================================== | 76%
|
|====================================================== | 77%
|
|====================================================== | 78%
|
|======================================================= | 78%
|
|======================================================= | 79%
|
|======================================================== | 79%
|
|======================================================== | 80%
|
|======================================================== | 81%
|
|========================================================= | 81%
|
|========================================================= | 82%
|
|========================================================== | 82%
|
|========================================================== | 83%
|
|=========================================================== | 84%
|
|=========================================================== | 85%
|
|============================================================ | 85%
|
|============================================================ | 86%
|
|============================================================= | 86%
|
|============================================================= | 87%
|
|============================================================= | 88%
|
|============================================================== | 88%
|
|============================================================== | 89%
|
|=============================================================== | 89%
|
|=============================================================== | 90%
|
|=============================================================== | 91%
|
|================================================================ | 91%
|
|================================================================ | 92%
|
|================================================================= | 93%
|
|================================================================== | 94%
|
|================================================================== | 95%
|
|=================================================================== | 96%
|
|==================================================================== | 97%
|
|==================================================================== | 98%
|
|===================================================================== | 98%
|
|===================================================================== | 99%
|
|======================================================================| 100%## Warning: 'TRUE' is not a valid name for counties in New YorkNYC_Income <- get_acs(
geography = "tract",
variables = "B19013_001", # Income
state = "NY",
county = c("Kings","Bronx","Queens","Richmond","New York"),
geometry = TRUE,
year = 2019
)## Getting data from the 2015-2019 5-year ACS## Warning: • You have not set a Census API key. Users without a key are limited to 500
## queries per day and may experience performance limitations.
## ℹ For best results, get a Census API key at http://api.census.gov/data/
## key_signup.html and then supply the key to the `census_api_key()` function to
## use it throughout your tidycensus session.
## This warning is displayed once per session.## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
|
| | 0%
|
|= | 1%
|
|= | 2%
|
|== | 3%
|
|=== | 4%
|
|=== | 5%
|
|==== | 5%
|
|===== | 7%
|
|===== | 8%
|
|======= | 9%
|
|======== | 11%
|
|========= | 13%
|
|========== | 14%
|
|========== | 15%
|
|============ | 17%
|
|============== | 20%
|
|================= | 24%
|
|=================== | 27%
|
|=================== | 28%
|
|==================== | 28%
|
|==================== | 29%
|
|====================== | 32%
|
|======================= | 33%
|
|========================= | 35%
|
|========================== | 37%
|
|============================ | 39%
|
|============================= | 41%
|
|============================= | 42%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 48%
|
|=================================== | 50%
|
|===================================== | 53%
|
|====================================== | 54%
|
|======================================== | 57%
|
|========================================= | 59%
|
|=========================================== | 61%
|
|=========================================== | 62%
|
|============================================ | 63%
|
|============================================= | 65%
|
|============================================== | 65%
|
|============================================== | 66%
|
|=============================================== | 67%
|
|================================================ | 68%
|
|================================================ | 69%
|
|================================================= | 71%
|
|=================================================== | 73%
|
|===================================================== | 75%
|
|===================================================== | 76%
|
|====================================================== | 77%
|
|======================================================= | 79%
|
|======================================================== | 80%
|
|========================================================= | 81%
|
|========================================================== | 83%
|
|============================================================ | 85%
|
|============================================================ | 86%
|
|=============================================================== | 89%
|
|================================================================ | 91%
|
|================================================================ | 92%
|
|================================================================= | 93%
|
|=================================================================== | 96%
|
|==================================================================== | 98%
|
|===================================================================== | 99%
|
|======================================================================| 100%NYC_Income_cleaned <- NYC_Income %>%
drop_na()
NYC_Tile <- get_static_tiles(
location = ny_tracts,
zoom = 9,
style_id = "light-v9",
username = "mapbox"
)## Attribution is required if using Mapbox tiles on a map.
## Add the text '(c) Mapbox, (c) OpenStreetMap' to your map for proper attribution.tm_shape(NYC_Tile) +
tm_rgb() +
tm_shape(NYC_Income_cleaned) +
tm_polygons(col = "estimate",
alpha = 0.5,
palette = "viridis",
title = "Median household income\n2015-2019 ACS",
lwd = 0.3) +
tm_layout(legend.outside = TRUE) +
tm_credits("Basemap credit to mapbox + openstreetmap", position = c("RIGHT","BOTTOM"))
vector_extract <- get_vector_tiles(
tileset_id = "mapbox.mapbox-streets-v8",
location = c(-73.99405, 40.72033),
zoom = 15
)
names(vector_extract)## [1] "building" "landuse" "place_label"
## [4] "poi_label" "road" "structure"
## [7] "transit_stop_label" "water"tmap_mode("view")## tmap mode set to interactive viewingtm_shape(vector_extract$landuse$polygons) +
tm_polygons(col = "type",alpha = 0.4)tm_shape(vector_extract$building$polygons) +
tm_polygons(col = "type",alpha = 0.4)#Leaflet package
factpal <- colorFactor(topo.colors(16), vector_extract$building$polygons$type)
leaflet() %>%
addMapboxTiles(style_id = "dark-v11",
username = "mapbox") %>%
setView(lng = -73.99405,
lat = 40.72033,
zoom = 16) %>%
addPolygons(data = vector_extract$building$polygons,
popup = vector_extract$building$polygons$type,
color = "white",
fillColor = ~factpal(type),
weight = 2)# Isochrones (service area analysis)
isochrones <- mb_isochrone("Chinatown, NYC",
time = c(4,8,12),
profile = "cycling")
library(mapdeck)##
## Attaching package: 'mapdeck'## The following objects are masked from 'package:googleway':
##
## add_geojson, add_heatmap, clear_geojson, clear_heatmap,
## invoke_method, melbourne, update_style## The following object is masked from 'package:tibble':
##
## add_columnmapdeck(style = mapdeck_style("dark")) %>%
add_polygon(data = isochrones,
fill_colour = "time",
fill_opacity = 0.5,
legend = TRUE)## Registered S3 method overwritten by 'jsonify':
## method from
## print.json jsonlite